Identification of Surrogate Models for the Prediction of Degrees of Freedom within a Tolerance Chain

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@Article{JANOUT:2023:procs,

• author = "Hannah Janout and Thomas Paier and Carina Ringelhahn and Michael Heckmann and Andreas Haghofer and Gabriel Kronberger and Stephan Winkler",
• title = "Identification of Surrogate Models for the Prediction of Degrees of Freedom within a Tolerance Chain",
• journal = "Procedia Computer Science",
• volume = "217",
• pages = "796--805",
• year = "2023",
• note = "4th International Conference on Industry 4.0 and Smart Manufacturing",
• ISSN = "1877-0509",
• DOI = "doi:10.1016/j.procs.2022.12.276",
• URL = "https://www.sciencedirect.com/science/article/pii/S1877050922023547",
• keywords = "genetic algorithms, genetic programming, Machine Learning, Surrogate Model, Gradient Boosted Tree, Neural Network, Robust Design, Tolerance Analysis, Symbolic Regression",
• abstract = "The computation of assembly tolerance information is necessary to fulfill robust design requirements. This assembly is computationally costly, with current calculations taking several hours. We aim to identify surrogate models for predicting degrees of freedom within a tolerance chain based on point connections between assembly components. Thus, replacing part of the current computation workflow and consequently reduce computation time. We use manufacturing tolerances set by norms and industrial standards to identifly these surrogate models, which define all relevant features and resulting output variables. We use black-box modeling methods (artificial neural networks and gradient boosted trees), as well as white-box modeling (symbolic regression by genetic programming). We see that these three models can reliably predict the degrees of freedom of a tolerance chain with high accuracy (R2 > 0.99)",

}

Genetic Programming entries for Hannah Janout Thomas Paier Carina Ringelhahn Michael Heckmann Andreas Haghofer Gabriel Kronberger Stephan M Winkler

Citations